Enhancing the Accuracy of Boresight Calibration with Coplanarity Constraint and Relative Height from DEM - Ran Guo; Yueming Wang (Remote Sensing, 2023)
As the resolution of airborne hyperspectral imagers (AHIs) continues to improve, the demand for accurate boresight calibration also increases. However, the high cost of ground control points (GCPs) and the low horizontal resolution of open digital elevation model (DEM) datasets limit the accuracy of AHI’s boresight calibration. We propose a method to enhance the accuracy of DEM-based boresight calibration using coplanarity constraints to address this issue. Our approach utilizes the relative accuracy of DEM in low-resolution DEM datasets. To make better use of the DEM, we apply coplanarity constraints to identify image features that display similar displacement in overlapping areas, and extract their corresponding elevation values from the DEM. These features and their relative heights are then incorporated into an optimization problem for boresight calibration. In the case of low-resolution DEM datasets, our method fully utilizes the relative accuracy of the DEM to improve the boresight correction precision. We have proven that the relative accuracy of elevation is more reliable than absolute accuracy in this situation. Our approach has been tested on the dataset from AHI, and the results have shown that the proposed method has better accuracy on low-resolution DEM datasets. In summary, our method provides a novel approach to improving the accuracy of DEM-based boresight calibration for AHIs, which can benefit applications, such as remote sensing and environmental monitoring. This research highlights the importance of utilizing the relative accuracy of low-resolution DEM datasets for improving the accuracy of boresight calibration.